Vivek R. Athalye

Reinforcement learning requires the selective modification of synapses that contribute to outcome-relevant neural activity, yet the principles underlying these selective modifications remain unclear. Here we combined longitudinal two-photon imaging of identified dendritic spines with a closed-loop optical brain–machine interface (BMI), in which activity from selected apical dendrites of layer 5 motor-cortical neurons controlled an auditory cursor, with target acquisition triggering optogenetic reinforcement. Mice learned to reliably generate the specific dendritic activity that leads to reinforcement. Learning was accompanied by selective strengthening and weakening of individual spines on reinforced dendrites. At individual synapses, the direction of plasticity depended on both the timing of activity relative to reinforced dendritic events and the pre-existing temporal organization of that activity. Furthermore, plasticity was enriched in small task-relevant synaptic groups: spines in synergistic groups were preferentially strengthened, whereas those in antagonistic groups were preferentially weakened. These changes were accompanied by the emergence of increasingly reproducible, selectively recruited, and temporally focused synaptic ensembles, and the incorporation of newly formed spines into these ensembles. Finally, blocking striatal dopamine receptor signaling impaired BMI learning and cortical synaptic reorganization of target dendrites, demonstrating a role for striatal reinforcement in cortical plasticity. Together, these findings reveal how reinforcement of specific dendritic activity sculpts synaptic ensembles that support learned operant dendritic control.

Animals can initiate movements either in response to external cues or from internal drive, yet how the brain flexibly supports both remains unclear. Disorders such as Parkinson’s disease disrupt these modes differently, suggesting distinct underlying mechanisms. These differences could arise from specialized circuits or from shared neuronal populations that shift their dynamics across contexts. To distinguish between these possibilities, we performed two-photon calcium imaging in the dorsolateral striatum as mice executed the same lever press either spontaneously or in response to a cue. Unsupervised clustering identified neurons modulated during cue, movement, or postaction periods. Critically, the same neurons encoded movement across initiation contexts, but their population dynamics diverged before movement. Both D1- and D2-expressing spiny projection neurons contributed to these dynamics, with D1-SPNs more active at the time of the sensory stimulus. These results show that context shapes neural dynamics within a shared movement-encoding population, revealing a context-generalizable striatal code that supports flexible movement initiation across internal and external drives.

The striatum is classically thought to modulate action vigor and reinforcement, but whether it specifies and controls ongoing actions at a fine scale remains unclear. Here, we test whether striatal activity can resolve and control actions as granular as distinct patterns of muscle co-contraction within the same limb. We developed a closed-loop system combining two-photon imaging and holographic optogenetics to identify and manipulate action-specific striatal ensembles in real time. Mice performed isometric push or pull actions engaging the same forelimb muscles with different activation patterns. Ensembles of both D1- and D2-spiny projection neurons encoded action identity with equal fidelity. Causal stimulation of small ensembles biased ongoing force, but only when the ensembles encoded the ongoing action. These results show that the striatum specifies and controls ongoing actions in real time at a previously unappreciated granularity, redefining its role in motor control.

Animals can initiate movements either in response to external cues or from internal drive, yet how the brain flexibly supports both remains unclear. Disorders such as Parkinson’s disease disrupt these modes differently, suggesting distinct underlying mechanisms. Such differences could arise from specialized circuits dedicated to each action mode or from shared neuronal populations that shift their dynamics across contexts. To distinguish between these possibilities, we performed two-photon calcium imaging in the dorsolateral striatum as mice executed the same lever press either spontaneously or in response to a cue, enabling direct comparison of internally and externally triggered actions. Unsupervised clustering revealed subpopulations of neurons modulated during cue, movement, or post-action periods. Critically, the same neurons were tuned to movement across initiation contexts. However, the dynamics of this shared population evolved within distinct “context” and “action” subspaces, even before movement initiation. Both dopamine D1- and D2-receptor-expressing spiny projection neurons contributed to both subspaces, with D1-SPNs more active at the time of the sensory stimulus. These results show that context shapes neural dynamics within a shared movement-encoding population, revealing a context-generalizable striatal code that supports flexible movement initiation across internal and external drives.

Reaching movements are critical for survival, and are learned and controlled by distributed motor networks. Even though the thalamus is a highly interconnected node in these networks, its role in learning and controlling reaches remains underexplored. We report dissociable roles of two thalamic forelimb circuits coursing through parafascicular (Pf) and ventroanterior/ventrolateral (VAL) nuclei in refining reaches to a spatial target. Using 2-photon calcium imaging as mice learn directional reaches, we observe high reach-related activity from both circuits early in learning, which decreases with learning. Pf activity encodes reach direction early in learning, more so than VAL. Consistently, bilateral lesions of Pf before training impairs refinement of reach direction. Pre-training lesions of VAL does not affect reach direction, but increases reach speed and target overshoot. Lesions of either nucleus after training does not affect the execution of learned reaches. These findings reveal different thalamic circuits governing distinct aspects of learned reaches.

Summary: It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it re-uses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface that transformed rhesus macaques’ motor cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low-dimensional, and critically, they align with the brain-machine interface, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements, and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

How to select and shape neural activity: When we learn a new skill or task, our movements are reinforced and shaped. Learning occurs because the neural activity patterns in the movement control–related brain regions that are rewarded are repeated. But how does this reinforcement work? Athalyeet al.developed a closed-loop self-stimulation paradigm in which a target motor cortical activity pattern resulted in the optogenetic stimulation of dopaminergic neurons. With training, mice learned to reenter specific neuronal activity patterns, which triggered self-stimulation and shaped their neural activity to be closer to the target pattern.Science, this issue p.1024